Transcriptome Analysis of Host-Associated Differentiation in \u3cem\u3eBemisia tabaci\u3c/em\u3e (Hemiptera: Aleyrodidae)

Host-associated differentiation is one of the driving forces behind the diversification of phytophagous insects. In this study, host induced transcriptomic differences were investigated in the sweetpotato whitefly Bemisia tabaci, an invasive agricultural pest worldwide. Comparative transcriptomic analyses using coding sequence (CDS), 5′ and 3′ untranslated regions (UTR) showed that sequence divergences between the original host plant, cabbage, and the derived hosts, including cotton, cucumber and tomato, were 0.11–0.14%, 0.19–0.26%, and 0.15–0.21%, respectively. In comparison to the derived hosts, 418 female and 303 male transcripts, respectively, were up-regulated in the original cabbage strain. Among them, 17 transcripts were consistently up-regulated in both female and male whiteflies originated from the cabbage host. Specifically, two ESTs annotated as Cathepsin B or Cathepsin B-like genes were significantly up-regulated in the original cabbage strain, representing a transcriptomic response to the dietary challenges imposed by the host shifting. Results from our transcriptome analysis, in conjunction with previous reports documenting the minor changes in their reproductive capacity, insecticide susceptibility, symbiotic composition and feeding behavior, suggest that the impact of host-associated differentiation in whiteflies is limited. Furthermore, it is unlikely the major factor contributing to their rapid range expansion/invasiveness

TTSVD: an efficient sparse decision making model with two-way trust recommendation in the AI enabled IoT systems

The convergence of AI and IoT enables data to be quickly explored and turned into vital decisions, and however, there are still some challenging issues to be further addressed. For example, lacking of enough data in AI-based decision making (so called Sparse Decision Making, SDM) will decrease the efficiency dramatically, or even disable the intelligent IoT networks. Taking the intelligent IoT networks as the network infrastructure, the recommendation systems have been facing such SDM problems. A naive solution is to introduce so-called trust information. However, trust information also maybe face the difficulty of sparse trust evidence (a.k.a sparse trust problem). In our work, an accurate sparse decision making model with two-way trust recommendation in the AI enabled IoT systems is proposed by us, named TT-SVD. Our model incorporates both trust information and rating information more completely, which can efficiently alleviate the above mentioned sparse trust problem and therefore be able to solve the cold start and data sparsity problems. Specifically, we first consider the two-fold trust influences from both trustees and trustors, which can be represented by a factor called trust propensity. To this end, we propose a dual model, including the trustor model (TrustorSVD) and a trustee model (TrusteeSVD) based on an existing rating-only recommendation model called SVD++, which are integrated by the weighted average and yield the final model, TT-SVD. The experimental results show that our model outperforms the state of the art including SVD and TrustSVD in both the ”all users” and ”cold start users” cases, and the accuracy improvement can reach a maximum of 29%. Complexity analysis shows that our model is equally suitable for the case of large sparse datasets. In a word, our model can effectively solve the sparse decision problem by introducing the two-way trust recommendation, and hence improve the efficiency of the intelligent recommendation systems

Transcriptomic Dissection of Sexual Differences in \u3cem\u3eBemisia tabaci\u3c/em\u3e, an Invasive Agricultural Pest Worldwide

Sex difference involving chromosomes and gene expression has been extensively documented. In this study, the gender difference in the sweetpotato whitefly Bemisia tabaci was investigated using Illumina-based transcriptomic analysis. Gender-based RNAseq data produced 27 Gb reads, and subsequent de novo assembly generated 93,948 transcripts with a N50 of 1,853 bp. A total of 1,351 differentially expressed genes were identified between male and female B. tabaci, and majority of them were female-biased. Pathway and GO enrichment experiments exhibited a gender-specific expression, including enriched translation in females, and enhanced structural constituent of cuticle in male whiteflies. In addition, a putative transformer2 gene (tra2) was cloned, and the structural feature and expression profile of tra2 were investigated. Sexually dimorphic transcriptome is an uncharted territory for the agricultural insect pests. Molecular understanding of sex determination in B. tabaci, an emerging invasive insect pest worldwide, will provide potential molecular target(s) for genetic pest control alternatives

DT-CP: a double-TTPs based contract-signing protocol with lower computational cost

This paper characterizes a contract signing protocol with high efficiency in Internet of Things. Recent studies show that existing contract-signing protocols can achieve abuse-freeness and resist inference attack, but cannot meet the high-efficiency and convenience requirement of the future Internet of things applications. To solve this problem, we propose a novel contract-signing protocol. Our proposed protocol includes two main parts: 1) we use the partial public key of the sender, instead of the zero-knowledge protocol, to verify the intermediate result; 2) we employ two independent Trusted Third Parties (TTPs) to prevent the honest-but-curious TTP. Our analysis shows that our double TTP protocol can not only result in lower computational cost, but also can achieve abuse-freeness with trapdoor commitment scheme. In a word, our proposed scheme performs better than the state of the art in terms of four metrics: encryption time, number of exponentiations, data to be exchanged and exchange steps in one round contract-signing

Highly pathogenic avian influenza H5N6 viruses exhibit enhanced affinity for human type sialic acid receptor and in-contact transmission in model ferrets

Since May 2014, highly pathogenic avian influenza H5N6 virus has been reported to cause six severe human infections three of which were fatal. The biological properties of this subtype, in particular its relative pathogenicity and transmissibility in mammals, are not known. We characterized the virus receptor-binding affinity, pathogenicity, and transmissibility in mice and ferrets of four H5N6 isolates derived from waterfowl in China from 2013-2014. All four H5N6 viruses have acquired a binding affinity for human-like SA alpha 2,6Gal-linked receptor to be able to attach to human tracheal epithelial and alveolar cells. The emergent H5N6 viruses, which share high sequence similarity with the human isolate A/Guangzhou/39715/2014 (H5N6), were fully infective and highly transmissible by direct contact in ferrets but showed less-severe pathogenicity than the parental H5N1 virus. The present results highlight the threat of emergent H5N6 viruses to poultry and human health and the need to closely track their continual adaptation in humans

MFF-AMD: multivariate feature fusion for Android malware detection

Researchers have turned their focus on leveraging either dynamic or static features extracted from applications to train AI algorithms to identify malware precisely. However, the adversarial techniques have been continuously evolving and meanwhile, the code structure and application function have been designed in complex format. This makes Android malware detection more challenging than before. Most of the existing detection methods may not work well on recent malware samples. In this paper, we aim at enhancing the detection accuracy of Android malware through machine learning techniques via the design and development of our system called MFF-AMD. In our system, we first extract various features through static and dynamic analysis and obtain a multiscale comprehensive feature set. Then, to achieve high classification performance, we introduce the Relief algorithm to fuse the features, and design four weight distribution algorithms to fuse base classifiers. Finally, we set the threshold to guide MFF-AMD to perform static or hybrid analysis on the malware samples. Our experiments performed on more than 25,000 applications from the recent five-year dataset demonstrate that MFF-AMD can effectively detect malware with high accuracy

A Secure Random Key Distribution Scheme Against Node Replication Attacks in Industrial Wireless Sensor Systems

With the widely deployment of wireless sensor networks in smart industrial systems, lots of unauthorized attacking from the adversary is greatly threating the security and privacy of the entire industrial systems, of which node replication attacks can hardly be defended since it is conducted in the physical layer. To solve this problem, we propose a secure random key distribution scheme, called SRKD, which provides a new method for the defense against the attack. Specifically, we combine a localized algorithm with a voting mechanism to support the detection and revocation of malicious nodes. We further change the meaning of the parameter s to help prevent the replication attack. Furthermore, the experimental results show that the detection ratio of replicate nodes exceeds 90% when number of network nodes reaches 200, which demonstrates the security and effectiveness of our scheme. Compared with existing state-of-the-art schemes, SRKD also has good storage and communication efficiency

Diagnostic extended usefulness of RMI: comparison of four risk of malignancy index in preoperative differentiation of borderline ovarian tumors and benign ovarian tumors

Abstract Background This study aimed to examine the performance of the four risk of malignancy index (RMI) in discriminating borderline ovarian tumors (BOTs) and benign ovarian masses in daily clinical practice. Methods A total of 162 women with BOTs and 379 women with benign ovarian tumors diagnosed at the Second Affiliated Hospital of Harbin Medical University from January 2012 to December 2016 were enrolled in this retrospective study. Also, we classified these patients into serous borderline ovarian tumor (SBOT) and mucinous borderline ovarian tumor (MBOT) subgroup. Preoperative ultrasound findings, cancer antigen 125 (CA125) and menopausal status were reviewed. The area under the curve (AUC) of receiver operator characteristic curves (ROC) and performance indices of RMI I, RMI II, RMI III and RMI IV were calculated and compared for discrimination between benign ovarian tumors and BOTs. Results RMI I had the highest AUC (0.825, 95% CI: 0.790–0.856) among the four RMIs in BOTs group. Similar results were found in SBOT (0.839, 95% CI: 0.804–0.871) and MBOT (0.791, 95% CI: 0.749–0.829) subgroups. RMI I had the highest specificity among the BOTs group (87.6, 95% CI: 83.9–90.7%), SBOT (87.6, 95% CI: 83.9–90.7%) and MBOT group (87.6, 95% CI: 83.9–90.7%). RMI II scored the highest overall in terms of sensitivity among the BOTs group (69.75, 95% CI: 62.1–76.7%), SBOT (74.34, 95% CI: 65.3–82.1%) and MBOT (59.18, 95% CI: 44.2–73.0%) group. Conclusion Compared to other RMIs, RMI I was the best-performed method for differentiation of BOTs from benign ovarian tumors. At the same time, RMI I also performed best in the discrimination SBOT from benign ovarian tumors